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The Identification of Relevant Attributes for Liver Cancer Therapies (IRALCT) project is intended to provide new insights into the relevant utility attributes regarding therapy choices for malignant primary and secondary liver tumors from the perspective of those who are involved in the decision-making process. It addresses the potential value of taking patients' expectations and preferences into account during the decision-making and, when possible, adapting therapies according to these preferences. Specifically, it is intended to identify the relevant clinical attributes that influence the patients', medical laymen's, and medical professionals' decisions and compare the three groups' preferences. We conducted maximum difference (MaxDiff) scaling among 261 participants (75 physicians, 97 patients with hepatic malignancies, and 89 medical laymen) to rank the importance of 14 attributes previously identified through a literature review. We evaluated the MaxDiff data using count analysis and hierarchical Bayes estimation (HB). Physicians, patients, and medical laymen assessed the same 7 attributes as the most important: probability (certainty) of a complete removal of the tumor, probability of reoccurrence of the disease, pathological evidence of tumor removal, possible complications during the medical intervention, welfare after the medical intervention, duration and intensity of the pain, and degree of difficulty of the medical intervention. The cumulative relative importance of these 7 attributes was 88.3%. Our results show that the physicians', patients', and medical laymen's preferences were very similar and stable.Trial registration DRKS-ID of the study: DRKS00013304, Date of Registration in DRKS: 2017/11/16.
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http://dx.doi.org/10.1038/s41598-022-23097-w | DOI Listing |
Neural Netw
September 2025
School of Electronic Science and Engineering, Nanjing University, China. Electronic address:
The Segment Anything Model (SAM) is a cornerstone of image segmentation, demonstrating exceptional performance across various applications, particularly in autonomous driving and medical imaging, where precise segmentation is crucial. However, SAM is vulnerable to adversarial attacks that can significantly impair its functionality through minor input perturbations. Traditional techniques, such as FGSM and PGD, are often ineffective in segmentation tasks due to their reliance on global perturbations that overlook spatial nuances.
View Article and Find Full Text PDFNeural Netw
September 2025
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China. Electronic address:
Automatic segmentation of retinal vessels from retinography images is crucial for timely clinical diagnosis. However, the high cost and specialized expertise required for annotating medical images often result in limited labeled datasets, which constrains the full potential of deep learning methods. Recent advances in self-supervised pretraining using unlabeled data have shown significant benefits for downstream tasks.
View Article and Find Full Text PDFNeural Netw
September 2025
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China.
3D shape defect detection plays an important role in autonomous industrial inspection. However, accurate detection of anomalies remains challenging due to the complexity of multimodal sensor data, especially when both color and structural information are required. In this work, we propose a lightweight inter-modality feature prediction framework that effectively utilizes multimodal fused features from the inputs of RGB, depth and point clouds for efficient 3D shape defect detection.
View Article and Find Full Text PDFParkinsonism Relat Disord
September 2025
Translational and Clinical Research Institute, Newcastle University, UK.
Introduction: Dysfunction of the glymphatic system is thought to lead to build up of toxic proteins including β-amyloid and α-synuclein, and thus may be involved in dementia with Lewy bodies (DLB) and Alzheimer's disease (AD). The Diffusion Tensor Image Analysis Along the Perivascular Space (DTI-ALPS) index has been proposed as a marker of glymphatic function.
Aims: To investigate DTI-ALPS in mild cognitive impairment (MCI) and dementia, and determine its relationship with cognitive decline, and biomarkers of neurodegeneration.
Eur J Oncol Nurs
August 2025
Koç University Hospital, Faculty of Medicine, Department of Medical Oncology, Istanbul, Türkiye. Electronic address:
Purpose: This study aimed to evaluate the effectiveness of a mobile chemotherapy drug guide application, ChemoNurse, developed for cancer nurses, in improving their knowledge and attitudes toward chemotherapy practices.
Methods: A randomized controlled trial with a repeated-measures design was conducted with 59 nurses (29 intervention, 30 control) who participated. Nurses in the intervention group used the ChemoNurse mobile application for six months, while the control group received no additional intervention.